Influence of growing locations, sample presentation technique and amount of foreign material on features extracted from colour images of Canada Western Red Spring wheat

Thumbnail Image
Zhang, Wanyu
Journal Title
Journal ISSN
Volume Title
An area scan colour camera was used to acquire images of single kernels of Canada Western Red Spring (CWRS) wheat from different growing locations (nine locations in the year 2007, eight locations in the years 2008 and 2009) in Western Canada. Two sample presentation methods were used. In the first method, fifteen kernels from a single location were imaged in a single image and in the second method one kernel from each location were imaged in the same image. Images of individual kernels of barley and rye were also acquired for a classification study. Bulk images of heaped and flat CWRS samples, heaped and flat barley samples, and images of CWRS wheat mixed with different proportion of foreign materials (0%, 2%, 5%, 10%, 20% barley) were acquired. Morphological, colour, and textural features from single kernel images and colour and textural features from bulk grain images were extracted by a program developed by researchers at the Canadian Wheat Board Centre for Grain Storage Research. The top 30 features from the single kernel images of CWRS wheat samples from different growing locations and also different crop years were compared by Scheffe's test. Image features from two types of presentation methods were also compared. Representative of a composite sample which was generated by randomly selecting kernels from each location was compared with individual locations. Three-way classification of CWRS wheat, barley, and rye was done using the top 30 features. For bulk grain image analysis, features from flat bulk grain samples and heaped bulk grain samples were extracted and compared. Image features of CWRS wheat mixed with different percentages of barley were examined, and a cross-validation discriminant classifier was developed to classify CWRS wheat mixed with different percentages of barley. Classifications were also conducted using flat grain as training, flat and heaped grain in testing. Results from this study indicated that most image features from different growing locations and also different crop year samples had significant differences. However, these differences did not influence three-way classification of CWRS wheat, barley, and rye. Features from the composite sample were compared with those from each location. Composite sample features were different from each location. Hence composite samples may not be representative for all locations. However three-way classification using composite sample features gave similar results as in the case of using each location samples. Canada Western Red Spring wheat and barley samples were used in comparing the image features of flat grain and heaped grain. Results indicated that image features from flat grain were different from heaped grain samples. However a two-way classification applied to heaped and flat CWRS wheat, and also heaped and flat barley, gave perfect classification accuracies. Classification models trained using flat grain also gave perfect classification accuracies when tested using flat and heaped grain. A comparison of the top 30 features extracted from images of CWRS wheat mixed with different proportion of barley revealed that grain image features changed after mixing barley. In classification of CWRS wheat mixed with 0, 2, 5, 10, and 20% barley, classification accuracies of 100, 99, 96, 95, and 98% were obtained, respectively.
machine vision, grain classification, grain feature